Incivility on platforms such as Twitter (now X) and Reddit complicates the development of AI systems that can support productive, rhetorically sound political argumentation. We present experiments with GPT-3.5 Turbo fine-tuned on two contrasting datasets of political discourse: high-incivility Twitter replies to U.S. Congress and low-incivility posts from Reddit’s r/ChangeMyView. Our evaluation examines how data composition and prompting strategies affect the rhetorical framing and deliberative quality of model-generated arguments. Results show that Reddit-finetuned models generate safer but rhetorically rigid arguments, while cross-platform fine-tuning amplifies adversarial tone and toxicity. Prompt-based steering reduces overt toxicity (e.g., personal attacks) but cannot fully offset the influence of noisy training data. We introduce a rhetorical evaluation rubric—covering justification, reciprocity, alignment, and authority—and provide implementation guidelines for authoring, moderation, and deliberation-support systems.

This paper describes the system for the last-min-submittion team in WASSA-2024 Shared Task 1:Empathy Detection and Emotion Classification. This task aims at developing models which can predict the empathy, emotion, and emotional polarity. This system achieved relatively goodresults on the competition’s official leaderboard.The code of this system is available here.

The AVeriTeC shared task introduces a new real-word claim verification dataset, where a system is tasked to verify a real-world claim based on the evidence found in the internet.In this paper, we proposed a claim verification pipeline called QueenVer which consists of 2 modules, Evidence Retrieval and Claim Verification.Our pipeline collects pairs of <Question, Answer> as the evidence. Recognizing the pivotal role of question quality in the evidence efficacy, we proposed question enrichment to enhance the retrieved evidence. Specifically, we adopt three different Question Generation (QG) technique, muti-hop, single-hop, and Fact-checker style. For the claim verification module, we integrate an ensemble of multiple state-of-the-art LLM to enhance its robustness.Experiments show that QueenVC achieves 0.41, 0.29, and 0.42 on Q, Q+A, and AVeriTeC scores.